1. Dr Paul Lewis Lecturer in Bioinformatics Lecturer in Bioinformatics Cardiff University Cardiff University Biostatistics & Bioinformatics Unit Biostatistics & Bioinformatics Unit

2. Biostatistics & Bioinformatics Unit (BBU) Bioinformatics resource for Institutions across Wales Backing of the Higher Education Funding Council for Wales - £1.5 million grant through the Research Capacity Development Fund UWCM, Cardiff University, Aberystwyth 13 new posts in statistics & bioinformatics MSc/Postgraduate Diploma/Postgraduate Certificate:  Bioinformatics  Genetic Epidemiology and Bioinformatics

3. Brief Overview of Microarray Bioinformatics Introduce My Microarray Research Interests My Microarray Analysis Software

4. Experimental Design Differential Gene Expression Hybridisation Data Pattern Discovery Class Prediction Annotation Normalisation Bioinformatics in Microarray Experiment

5. Normalization Remove non-biological influences on data (systematic variation) 3 categories of Normalisation Normalisation – transform data to make more like a normal distribution log, lowess, linlog Standardisation – expand or contract distribution so data from different experiments can be compared calculate Z-scores Centralisation – move distribution so its centered around expected mean mean / median / mean trimmed centering

6. Experimental Design Differential Gene Expression Hybridisation Data Pattern Discovery Class Prediction Annotation Normalisation Bioinformatics in Microarray Experiment

7. With Replicates Parametric tests t-test (ANOVA) J. Comput. Biol. 2000 7: 817-838 Bayesian t-test Bioinformatics 2001 17: 509-519. Mixture modelling & bootstrapping(SAM) P.N.A.S. 2001 98: 5116-5121 Regression modelling Genome Res. 2001 11: 1227-1236. All give similar results but SAM reduces false positives Non Parametric Tests Wilcoxon rank sum test Bioinformatics 2002 18: 1454-1461 Non-parametric t-test Bioinformatics 2002 18: 1454-1461 Ideal discriminator method Bioinformatics 2002 18: 1454-1461 low false positive rate but less power Find Differentially Expressed Genes Is fold change significant?

8. Experimental Design Differential Gene Expression Hybridisation Data Pattern Discovery Class Prediction Annotation Normalisation Bioinformatics in Microarray Experiment

9. Pattern Discovery & Class Prediction Explore how genes or samples group: Clustering Hierarchical Cluster AnalysisHIERARCHY K-Means Self Organising Maps (SOM)PARTITION Fuzzy ART Principal Components Analysis (PCA) Multidimensional Scaling (MDS)REDUCTION Correspondence Analysis (CoA) Assign genes to known groupings: Classification logistic regression neural networks linear discriminant analysis

10. Hierarchical Cluster Analysis

11. Partitioning Clustering Methods Need To Tell Methods Number of Clusters Genes Partitioned into Clusters What are Relationships Between Clusters? K-Means & SOM

12. 2D & 3D Mapping Methods CoA MDS PCA Data Projected onto 2 or 3 Dimensions But….What are Cluster Boundaries?

13. Experimental Design Differential Gene Expression Hybridisation Data Pattern Discovery Class Prediction Annotation Normalisation Bioinformatics in Microarray Experiment

14. Online Tools: ARROGANT http://lethargy.swmed.edu/ DAVID http://apps1.niaid.nih.gov/david/ DRAGON http://207.123.190.10/dragon.htm EASE http://apps1.niaid.nih.gov/david/ FANTOM http://www.gsc.riken.go.jp/e/FANTOM/ GoMiner http://discover.nci.nih.gov/gominer/ MatchMiner http://discover.nci.nih.gov/matchminer/ Onto-Express http://vortex.cs.wayne.edu/Projects.html RESOURCERER http://pga.tigr.org/tigr-scripts/magic/r1.pl Affymetrix GO http://www.affymetrix.com Databases: Gene Ontology http://www.geneontology.org/ OMIM http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM LocusLink http://www.ncbi.nlm.nih.gov/LocusLink/ UniGene http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM LocusLink http://www.ncbi.nlm.nih.gov/LocusLink/ Annotation

15. My Research Interests Pattern Discovery Algorithm Development Biologist-Friendly Software Tools Take - 2D & 3D Mapping Methods Methods - Define Cluster Boundaries Make FUZZY EAS-IEAS-I 2D & 3D Visualisation Tools

16. Cluster Boundaries CoA MDS PCA

17. Fuzzy Clustering Differs to standard clust by assigning membership of a gene to all clusters Allows you to see the association of each gene within a cluster Can calculate the number of clusters in Partitioning methods (Fuzzy ART) Helps Combine Clusters Helps to clear Ambiguity

18. Fuzzy Mapping Add Membership values of each gene to clusters

19. Fuzzy Partitioning K-Means & SOM

21. Need for Comprehensive Pattern Discovery Software Suite Fuzzy Data Analysis Suite Visualisation Tools to explore data Easy to use Free Microarray Pattern Discovery BBUnit Web based version Service by BBU Increase traffic to BBU web site Establish BBU for microarray Cross platform

22. INTERFACE Normalisation Differential Gene Expression Pattern Discovery Utilities Log Normalise Mean Centre Median centre T test ANOVA Regression Hierarchical Cluster Analysis SOM K-Means Fuzzy Art PCA MDS CoA Fuzzy C-Means

30. http://bbu.uwcm.ac.uk lewispd@cf.ac.uk Contact

31. Pete Kille Alan Clarke Gareth Hughes(EASI team) Karen Reed(Data) Lesley Jones(Data, & EASI Collaborator) BBU Acknowledgements